Water Resources in Copenhagen during 20th century - 01
This script visualizes the spatial component of the data accompanying the Spring 2021 course on the City: Between Culture and Nature, taught by Mikkel Thelle and Mikkel Høghøj. The course surveys the gradual appearance of private and public bathing facilities, toilets and communal hygienic resources in the city of Copenhagen during the 20th century. By editing elements in this script, you can explore different aspects of past and present hygienic amenities in the capital of Denmark.
Before we start: data wrangling
First load the packages necessary for spatial data visualisation and analysis.
Spatial data
Next, load your spatial data - polygons representing the suburbs of Copenhagen.
suburbs <- st_read("data/bydel.shp", options = "ENCODING=WINDOWS-1252") # thank you, Malte, for the options argument which fixed the Danish charsoptions: ENCODING=WINDOWS-1252
Reading layer `bydel' from data source `C:\Users\Adela\Documents\RStudio\1_Teaching\BetweenCultureAndNature\data\bydel.shp' using driver `ESRI Shapefile'
Simple feature collection with 10 features and 4 fields
geometry type: MULTIPOLYGON
dimension: XY
bbox: xmin: 12.45305 ymin: 55.61284 xmax: 12.73425 ymax: 55.73271
geographic CRS: WGS 84
Simple feature collection with 6 features and 4 fields
geometry type: MULTIPOLYGON
dimension: XY
bbox: xmin: 12.46344 ymin: 55.63174 xmax: 12.73425 ymax: 55.73271
geographic CRS: WGS 84
id bydel_nr navn areal_m2 geometry
5 6 6 Vanløse 6699011 MULTIPOLYGON (((12.4982 55....
6 5 5 Valby 9234177 MULTIPOLYGON (((12.52434 55...
7 4 4 Vesterbro-Kongens Enghave 8472602 MULTIPOLYGON (((12.54553 55...
8 1 1 Indre By 10488466 MULTIPOLYGON (((12.72897 55...
9 9 9 Amager Øst 9820410 MULTIPOLYGON (((12.63082 55...
10 2 2 Østerbro 9858740 MULTIPOLYGON (((12.59777 55... [1] 3 7 8 10 6 5 4 1 9 2Attribute data
Next let’s bring in the attribute data. I read the data from a google sheet where my colleagues and I can edit it. You can load it from there if you have a googlesheets4 package installed, or you can use the read_csv() function to read the wc.csv provided in the data folder
# Uncomment the lines below to read data from GDrive wc <- read_sheet('https://docs.google.com/spreadsheets/d/1iFvycp6M6bF8GBkGjA2Yde2yCIhiy5_slAkGF-RUF7w/edit#gid=0', col_types = 'cnnnnnnnn')
# write_csv(wc, 'data/wc.csv')
wc <- read_csv("data/wc.csv")
wc# A tibble: 100 x 9
Suburb suburb_id flats wc_access bath year bath_communal_ct wc_communal_ct hot_water
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Indre by 1 16280 11310 3800 1950 NA NA 3390
2 Christianshavn 1 5490 3900 900 1950 NA NA 850
3 Voldkvartererne 1 13460 12690 4560 1950 NA NA 4290
4 Østerbro 2 30820 28900 13750 1950 NA NA 10750
5 Indre Nørrebro 3 28700 24380 5910 1950 NA NA 4010
6 Ydre Nørrebro 3 21710 20410 5800 1950 NA NA 4800
7 Vesterbro 4 25850 23930 3730 1950 NA NA 2480
8 Kongens Enghave 4 6270 6240 4240 1950 NA NA 3630
9 Valby 5 14430 13970 8190 1950 NA NA 6980
10 Vigerslev 5 7700 7580 5050 1950 NA NA 3850
# ... with 90 more rowsSpatial resolution adjustment - data aggregation
Data on access to hygienic facilities and other water resources in Copenhagen now looks good and tidy, but its spatial resolution is higher than the provided polygons (as in we have multiple rows that all fit within one suburb id). We therefore use the group_by() function to aggregate the data by id before we continue with any spatial operations. Given that the dataset is in fact a time-series, and each kvarter has a record for a given year or decade, we need to group first by the year and then only by id.
While aggregating the finer scale data into larger units, it is convenient to generate some statistics, such as percentages of flats that have bath and wc and hot water access within each suburb. We do this using the summarize() function below.
wcdata <- wc %>% group_by(year, suburb_id) %>% summarize(flats = sum(flats), bath = sum(bath), pct_bath = bath/flats * 100, wc_access = sum(wc_access), pct_wc = wc_access/flats * 100, warmH20 = sum(hot_water),
pct_wH20 = warmH20/flats * 100, communal_wc = sum(wc_communal_ct), communal_bath = sum(bath_communal_ct))
wcdata# A tibble: 50 x 11
# Groups: year [5]
year suburb_id flats bath pct_bath wc_access pct_wc warmH20 pct_wH20 communal_wc communal_bath
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1950 1 35230 9260 26.3 27900 79.2 8530 24.2 NA NA
2 1950 2 30820 13750 44.6 28900 93.8 10750 34.9 NA NA
3 1950 3 50410 11710 23.2 44790 88.9 8810 17.5 NA NA
4 1950 4 32120 7970 24.8 30170 93.9 6110 19.0 NA NA
5 1950 5 22130 13240 59.8 21550 97.4 10830 48.9 NA NA
6 1950 6 10260 6780 66.1 10120 98.6 6270 61.1 NA NA
7 1950 7 27260 14790 54.3 26770 98.2 13280 48.7 NA NA
8 1950 8 19270 15000 77.8 18980 98.5 14690 76.2 NA NA
9 1950 9 23960 12470 52.0 22950 95.8 11210 46.8 NA NA
10 1950 10 18000 9030 50.2 16200 90 7800 43.3 NA NA
# ... with 40 more rowsJoin the aggregated attribute data with its spatial representations
Now we can join the data with the spatial polygons
Simple feature collection with 50 features and 14 fields
geometry type: MULTIPOLYGON
dimension: XY
bbox: xmin: 12.45305 ymin: 55.61284 xmax: 12.73425 ymax: 55.73271
geographic CRS: WGS 84
First 10 features:
id bydel_nr navn areal_m2 year flats bath pct_bath wc_access pct_wc warmH20 pct_wH20 communal_wc communal_bath geometry
1 1 1 Indre By 10488466 1981 26413 14035 53.13671 22546 85.35948 NA NA NA NA MULTIPOLYGON (((12.72897 55...
2 1 1 Indre By 10488466 1950 35230 9260 26.28442 27900 79.19387 8530 24.21232 NA NA MULTIPOLYGON (((12.72897 55...
3 1 1 Indre By 10488466 1965 32470 12780 39.35941 32450 99.93840 NA NA 9510 2280 MULTIPOLYGON (((12.72897 55...
4 1 1 Indre By 10488466 1970 30440 11386 37.40473 22381 73.52497 NA NA NA NA MULTIPOLYGON (((12.72897 55...
5 1 1 Indre By 10488466 1955 33490 9740 29.08331 33430 99.82084 9130 27.26187 NA NA MULTIPOLYGON (((12.72897 55...
6 2 2 Østerbro 9858740 1950 30820 13750 44.61389 28900 93.77028 10750 34.87995 NA NA MULTIPOLYGON (((12.59777 55...
7 2 2 Østerbro 9858740 1955 30540 14060 46.03798 30510 99.90177 11660 38.17944 NA NA MULTIPOLYGON (((12.59777 55...
8 2 2 Østerbro 9858740 1965 32210 17340 53.83421 32190 99.93791 NA NA 4670 920 MULTIPOLYGON (((12.59777 55...
9 2 2 Østerbro 9858740 1970 32464 17728 54.60818 28139 86.67755 NA NA NA NA MULTIPOLYGON (((12.59777 55...
10 2 2 Østerbro 9858740 1981 32448 20255 62.42295 29343 90.43084 NA NA NA NA MULTIPOLYGON (((12.59777 55...Now that we have a merged spatial dataset with attributes, let’s review what attributes are available for visualisation
[1] "id" "bydel_nr" "navn" "areal_m2" "year" "flats" "bath" "pct_bath" "wc_access" "pct_wc" "warmH20" "pct_wH20"
[13] "communal_wc" "communal_bath" "geometry" There is the suburb polygon data, such as id, bydel_nr, navn and areal_m2, and there is also the attribute data such as year, flats, bath ,etc. This gives us lots of choices for display. Lets put the data in a map.
Plot the data on the map
Let’s start by plotting one year alone, to learn how the map works.
Flats and water resources in 1950
Run the whole chunk below, and once it renders, look at the map. Afterwards, try changing the definition of what is to be displayed on line 116. For example, replace "flats" for some other column, such as "pct_bath", or "wc_access" to see how the map changes. To modify the legend, you can modify line 118 where we describe style. Replace style = "jenks" with "pretty", or "equal", or "quantile". What happens to your classification?
wc1950 <- wc_spatial %>% filter(year == 1950)
library(tmap)
tmap_mode(mode = "plot")
tm_shape(wc1950) + tm_borders(col = "black", lwd = 1) + tm_polygons("flats", id = "navn", style = "jenks") + tm_legend(legend.position = c("RIGHT", "TOP")) + tm_compass(position = c("RIGHT", "BOTTOM"),
type = "rose", size = 2) + tm_scale_bar(position = c("RIGHT", "BOTTOM"), breaks = c(0, 2, 4), text.size = 1) + tm_credits(position = c("RIGHT", "BOTTOM"), text = "Adela Sobotkova, 2021") + tm_layout(main.title = "Copenhagen 1950 situation",
legend.outside = FALSE)
Flats through time
Now, that you have mastered visualization of a single year, let’s plot all the years we have available!
tmap_options(limits = c(facets.view = 5)) # we want to view 5 periods
tmap_mode(mode = "view")
tm_shape(wc_spatial) + tm_facets(by = "year", ncol = 5) + tm_polygons("flats", id = "navn", style = "jenks") + tm_layout(main.title = "Copenhagen Flats", legend.outside = TRUE)Flats per square kilometer
Now that we have a spatial object, we can create new columns, for example utilizing the shape area to calculate the density of flats per sq km.
And now we can view these new columns in the map
tmap_mode(mode = "view")
tm_shape(wc_spatial) + tm_facets(by = "year", ncol = 5) + tm_polygons("flat_per_km", n = 5, style = "jenks") #+
Access to private baths: percentages of households that have it per suburb
Lets calculate the baths and toilets available per square kilometer per each suburb
tmap_mode(mode = "view")
tm_shape(wc_spatial) + tm_facets(by = "year", ncol = 5) + tm_polygons("pct_bath", id = "navn", style = "pretty", title = "% of flats with <br> access to bath")
Access to private toilets: percentages of households that have it per suburb
library(tmap)
tmap_options(limits = c(facets.view = 5)) # we want to view 5 periods
tmap_mode(mode = "view")
tm_shape(wc_spatial) + tm_facets(by = "year", ncol = 5) + tm_polygons("pct_wc", id = "navn", style = "pretty", title = "% of flats with <br>access to WC")
Follow the example and develop your own analysis and map
You can also calculate the number of baths and toilets per square kilometer and plot in the map
..or continue with communal resources and warm water
Why not practice and try plotting the flats that have access to communal baths and wc, and or hot water? Create your own map here, following the examples above.